Medical imaging encompasses techniques and processes used to create images of the human body for clinical purposes, including medical procedures for diagnosing or monitoring disease. Medical imaging technology has grown to encompass many image recording techniques including electron microscopy, fluoroscopy, magnetic resonance imaging (MRI), nuclear medicine, photoacoustic imaging, positron emission tomography (PET), projection radiography, thermography, computed tomography (CT), and ultrasound.
Medical imaging can incorporate the use of compounds referred to as contrast agents or contrast materials to improve the visibility of internal bodily structures in an image.
Medical imaging was originally restricted to acquisition of singular static images to capture the anatomy of an organ/region of the body. Currently, the use of more sophisticated imaging techniques allows dynamic studies to be made that provide a temporal sequence of images which can characterize physiological or pathophysiological information.
Dynamic medical imaging involves an acquisition process that takes many “snapshots” of the organ/region/body of interest over time in order to capture a time-varying behaviour, for example, distribution of a contrast agent and hence capture of a specific biological state (disease, condition, physiological phenomenon, etc.). As the speed and digital nature of medical imaging evolves, this acquisition data can have tremendous temporal resolution and can result in large quantities of data.
Medical imaging technologies have been used widely to improve diagnosis and care for such conditions as cancer, heart disease, brain disorders, and cardiovascular conditions. Most estimates conclude that millions of lives have been saved or dramatically improved as a result of these medical imaging technologies. However, the risk of radiation exposure from such medical imaging technologies for patients must be considered.
In this regard, the increasing use of CT in medical diagnosis has highlighted concern about the increased cancer risk to exposed patients because of the larger radiation doses delivered in CT than the more common, conventional x-ray imaging procedures. This is particularly true with the two-phase CT Perfusion protocol. For illustration purposes, using the dose-length product for a protocol provided by a commercially available CT scanner (GE Healthcare), the effective dose of a CT Stroke series consisting of: 1) a non-enhanced CT scan to rule out hemorrhage; 2) a CT angiography to localize the occlusion causing the stroke; and 3) a two-phase CT Perfusion protocol to define the ischemic region with blood flow and blood volume and predict hemorrhagic transformation (HT) with blood-brain barrier permeability surface area product (BBB-PS); is 10 mSv, of which 4.9 mSv is contributed by the two-phase CT Perfusion protocol. The CT Stroke series is predicted to induce 80 and 131 additional cancers for every exposed 100,000 male and female acute ischemic stroke (AIS) patients, respectively, with the two-phase CT Perfusion protocol alone predicted to cause half of the additional cancers (Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII. The National Academies Press, Washington D.C., 2006).
Unless the effective dose of the two-phase CT Perfusion protocol is reduced, the benefits of medical imaging will be undermined by the concern over cancer induction. Furthermore, the concern over the risks of radiation exposure extends to other medical imaging techniques, particularly for pediatric patients or those patients undergoing repeated exposure.
The use of statistical filtering techniques to increase the signal to noise ratio in low dose radiation imaging has been described, for example in U.S. Pat. No. 7,187,794 issued Mar. 6, 2007. However, at present the application of statistical filtering is limited to projection data prior to the construction of images. Several disadvantages are associated with statistical filtering of projection data. For example, the computational burden when working with projection data is high because each image from a dynamic sequence is typically reconstructed from ˜1,000 projections. In addition, the filtering process has to be ‘hardwired’ into the image reconstruction pipeline of the scanner. As such, statistical filters of projection data lack flexibility as they are typically specific to a scanner platform.
Whether perceived or proven, concerns over the risk of radiation exposure will have to be addressed to assure patients of the long term safety of medical imaging techniques. Accordingly, there is a need for medical imaging techniques that allow for a reduction in radiation dose.
It is therefore an object of the present invention to provide a novel system and method for processing images.